What quantization should I use for Qwen3-8B on a Mac mini M4 24GB?

Q8_0 is the highest-quality quant that grades B or better on this hardware with llama.cpp at 8K context; the per-quant table on this page grades all 4 available quants.

Can a Mac mini M4 24GB run Qwen3-8B?

Yes. Qwen3-8B needs 10.4GB at Q8_0 with llama.cpp and 8K context; the Mac mini M4 24GB has 24GB of unified memory (~75% GPU-addressable) (17.5GB usable) — grade A, with decode speed in the painful tier.

Mac mini M4 24GB · Qwen3-8B · Q8_0

✅ Fits — grade A

Painful

weights 8.71 GBKV cache 1.21 GBoverhead 0.48 GBpool 17.5 GBneeds 10.4 GB

assumes llama.cpp · batch 1 · 8K context · fp16 KV cache

Also runs with: Ollama (one-line install) · LM Studio (GUI) · KoboldCpp (GUI)

Rent a GPU that fits

It fits, but only in the Painful tier — usable for a batch job, rough for anything interactive. If you need it responsive, renting a larger GPU by the hour is worth pricing against an upgrade.

Browse GPUs on Vast.ai

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Adjust assumptions — runtime, context, GPU count →

Every quant, graded

Mac mini M4 24GB × Qwen3-8B, llama.cpp, 8K context.

Quant	File size	Fit	Speed tier
Q8_0	8.7 GB	Fits · A	painful
Q6_K	6.7 GB	Fits · A	usable
Q5_K_M	5.8 GB	Fits · A	usable
Q4_K_M	5.0 GB	Fits · A	usable

Qwen3-8B on similar hardware

More models on the Mac mini M4 24GB

Verdict computed from published specs and measured GGUF file sizes — see how the math works.